How to Import Multiple CSV Files into One DataFrame with Different Column Names in Python Pandas

Learn how to efficiently combine multiple CSV files into a single DataFrame in Pandas while renaming columns and excluding unimportant ones. --- This video is based on the question https://stackoverflow.com/q/63672127/ asked by the user 'Ewdlam' ( https://stackoverflow.com/u/12292032/ ) and on the answer https://stackoverflow.com/a/63672178/ provided by the user 'BENY' ( https://stackoverflow.com/u/7964527/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to import multiple csv in one dataframe with different column names and unimportant columns? Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- How to Import Multiple CSV Files into One DataFrame with Different Column Names When working with data in Python, especially in data science and data analysis tasks, it’s common to encounter situations where you need to merge multiple CSV files into a single DataFrame. However, this process can get tricky when the files have different column names and contain unnecessary data. In this article, we'll guide you through the process of importing multiple CSV files while renaming columns and excluding unimportant ones using Pandas. The Problem: Different Column Names and Unimportant Columns Imagine you have two CSV files in one directory, and when you import them separately, you get two distinct DataFrames: First CSV DataFrame [[See Video to Reveal this Text or Code Snippet]] Second CSV DataFrame [[See Video to Reveal this Text or Code Snippet]] You want to combine these two DataFrames into one, and since the column names differ, you will use a dictionary to map the columns from the second file to the names you want in the combined DataFrame: Mapping Dictionary [[See Video to Reveal this Text or Code Snippet]] Step-by-Step Solution: Merging CSV Files Now, let’s employ Pandas to seamlessly merge these files and rename the columns according to your mapping. Here’s how you can do it: Step 1: Import Necessary Libraries Start by importing the required libraries: [[See Video to Reveal this Text or Code Snippet]] Step 2: Define the Path to Your CSV Files Next, define the path to the directory where your CSV files are stored: [[See Video to Reveal this Text or Code Snippet]] Step 3: Read and Combine the CSV Files Use the pd.concat function alongside rename to modify the column names per your mapping: [[See Video to Reveal this Text or Code Snippet]] In this snippet: We loop through each file found in the directory. We read each CSV file. We rename the columns according to our Dict_col_names mapping. Finally, these DataFrames are combined into one. Step 4: Handling Different Column Structures If the two CSV files have differing columns and you wish to include only the columns that are common, you can add join='inner' to the concatenate method: [[See Video to Reveal this Text or Code Snippet]] Expected Output By carefully following these steps, you should attain a unified DataFrame that looks like this: [[See Video to Reveal this Text or Code Snippet]] Conclusion Combining multiple CSV files with different column names in Python’s Pandas library doesn’t have to be overwhelming. With just a few steps—setting the file path, reading the files, renaming the columns, and merging—you can create a clean and organized DataFrame ready for analysis. This method is quite efficient, especially when dealing with large datasets and multiple files. With this guide, you can now manage and integrate your data with ease, leaving more time for data analysis and insights rather than wrestling with imports. Happy coding!